Search

Under polar and subpolar climatic conditions, volcano edifice growth and stability are affected by extreme erosion rates, mass wasting, glacier loading (and unloading), and permafrost soil conditions. Relatively small changes in temperature can lead to very different snow and ice conditions in relation to all of the above. Therefore active, shallow magmatic plumbing systems and magmatic pathways might react sensitively to even minor changes of their surrounding environmental conditions. Almost constant degassing from the summit crater of Mount Curry (Zavodovski Island) and the presence of an active lava lake within the summit crater of Mount Michael (Saunders Island) suggest the existence of shallow magmatic plumbing systems at both volcanoes. They therefore represent exceptional study sites for investigating volcano processes under subpolar climatic conditions. Because of their remoteness, none of these islands are equipped with permanently installed ground-based instruments. We observe and quantify surface displacements related to volcanic activity, fumarolic activity, tectonic activity in the Scotia arc, as well as glacier flow from high-resolution combined TerraSAR-X and PAZ interferometry and amplitude offsets. Multi-temporal topographic data are available through the TanDEM-X SAR satellite mission and photogrammetric surveys conducted in April-Mai 2019 at Saunders Island and in January-February 2023 on Zavodovski Island. Here we introduce the first results of combining and exploring UAV photogrammetry with SAR satellite data. We present a geomorphological and structural analysis of Zavodovski Island and the outer subaerial and shallower submarine flanks of Saunders Island. We also estimate the glacier volume and volume change over time on Saunders as well as surface dynamics at Zavodovski. With this study we highlight the unprecedented detail and the valuable information that can be retrieved from tasked and targeted TerraSAR-X, TanDEM-X, and PAZ satellite acquisitions coupled

Animal-borne telemetry devices provide essential insights into the life-history strategies of far-ranging species and allow us to understand how they interact with their environment. Many species in the seabird family Alcidae undergo a synchronous moult of all primary flight feathers during the non-breeding season, making them flightless and more susceptible to environmental stressors, including severe storms and prey shortages. However, the timing and location of moult remains largely unknown, with most information coming from studies on birds killed by storms or shot at sea. Using light-level geolocators with saltwater immersion loggers, we develop a method for determining flightless periods in the context of the annual cycle. Four Atlantic puffins (Fratercula arctica) were equipped with geolocator/immersion loggers on each leg to attempt to overcome issues of leg-tucking in plumage while sitting on the water, which confounds the interpretation of logger data. Light level and saltwater immersion time-series data were combined to correct for this issue. This approach was adapted and applied to 40 puffins equipped with the standard practice deployments of geolocators on one leg only. Flightless periods consistent with moult were identified in the dual-equipped birds, whereas moult identification in single-equipped birds was less definitive and should be treated with caution. Within the dual-equipped sample, we present evidence for two flightless moult periods per non-breeding season in two puffins that undertook more extensive migrations (> 2000km), and were flightless for up to 76 days in a single non-breeding season. A biannual flight feather moult is highly unusual among non-passerine birds, and may be unique to birds that undergo catastrophic moult, i.e. become flightless when moulting. Though our conclusions are based on a small sample, we have established a freely available methodological framework for future investigation of the moult patterns of this and other seabird species.

If not carefully managed, harvesting of krill risks disturbing the ecological balance of many Antarctic and sub-Antarctic sites where krill-dependent predators feed. One of the least disturbed sites anywhere within the Southern Ocean and one where krill fishing has so far been virtually non-existent is the South Sandwich Islands volcanic archipelago. Some of the main krill predators breeding at the South Sandwich Islands are penguins, with five species breeding on the islands, the dominant species of which is the chinstrap penguin. In this paper we report on the results of ARGOS PTT deployments during the chinstrap penguin chick-rearing period, using the recorded foraging trips to develop habitat models. Foraging habitats used by chinstrap penguins during the chick-rearing period were best characterised by distance from the colony and sea surface temperature and, using these two covariates, we predicted foraging habitat use around all islands. We show that the provisions of the South Georgia and South Sandwich Islands Marine Protected Area ensure that chinstrap penguins, and other krill-dependent predators with similar foraging ranges, likely have robust protection during the summer. During the winter, when krill predators are likely to forage further offshore, seasonal sea ice provides a physical barrier to exclude the fishery, again ensuring the islands’ unique biodiversity receives strong protection. However, to the north of the marginal sea ice zone, competition between krill predators and the fishery could exist if the fishery were ever to explore new locations for resource extraction. We make a number of conclusions, including the need for winter tracking data to inform future management options.

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it requires large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increases these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.

Penguins (Sphenisciformes) are an iconic order of flightless, diving seabirds distributed across a large latitudinal range in the Southern Hemisphere. The extensive area over which penguins are endemic is likely to have fostered variation in pathogen pressure, which in turn will have imposed differential selective pressures on the penguin immune system. At the front line of pathogen detection and response, the Toll-like receptors (TLRs) provide insight into host evolution in the face of microbial challenge. TLRs respond to conserved pathogen-associated molecular patterns and are frequently found to be under positive selection, despite retaining specificity for defined agonist classes. We undertook a comparative immunogenetics analysis of TLRs for all penguin species and found evidence of adaptive evolution that was largely restricted to the cell surface-expressed TLRs, with evidence of positive selection at, or near, key agonist-binding sites in TLR1B, TLR4, and TLR5. Intriguingly, TLR15, which is activated by fungal products, appeared to have been pseudogenized multiple times in the Eudyptes spp., but a full-length form was present as a rare haplotype at the population level. However, in vitro analysis revealed that even the full-length form of Eudyptes TLR15 was nonfunctional, indicating an ancestral cryptic pseudogenization prior to its eventual disruption multiple times in the Eudyptes lineage. This unusual pseudogenization event could provide an insight into immune adaptation to fungal pathogens such as Aspergillus, which is responsible for significant mortality in wild and captive bird populations.

Recognizing the imperative to evaluate species recovery and conservation impact, in 2012 the International Union for Conservation of Nature (IUCN) called for development of a "Green List of Species" (now the IUCN Green Status of Species). A draft Green Status framework for assessing species' progress toward recovery, published in 2018, proposed 2 separate but interlinked components: a standardized method (i.e., measurement against benchmarks of species' viability, functionality, and preimpact distribution) to determine current species recovery status (herein species recovery score) and application of that method to estimate past and potential future impacts of conservation based on 4 metrics (conservation legacy, conservation dependence, conservation gain, and recovery potential). We tested the framework with 181 species representing diverse taxa, life histories, biomes, and IUCN Red List categories (extinction risk). Based on the observed distribution of species' recovery scores, we propose the following species recovery categories: fully recovered, slightly depleted, moderately depleted, largely depleted, critically depleted, extinct in the wild, and indeterminate. Fifty-nine percent of tested species were considered largely or critically depleted. Although there was a negative relationship between extinction risk and species recovery score, variation was considerable. Some species in lower risk categories were assessed as farther from recovery than those at higher risk. This emphasizes that species recovery is conceptually different from extinction risk and reinforces the utility of the IUCN Green Status of Species to more fully understand species conservation status. Although extinction risk did not predict conservation legacy, conservation dependence, or conservation gain, it was positively correlated with recovery potential. Only 1.7% of tested species were categorized as zero across all 4 of these conservation impact metrics, indicating that conservation has, or will, play a role in improving or maintaining species status for the vast majority of these species. Based on our results, we devised an updated assessment framework that introduces the option of using a dynamic baseline to assess future impacts of conservation over the short term to avoid misleading results which were generated in a small number of cases, and redefines short term as 10 years to better align with conservation planning. These changes are reflected in the IUCN Green Status of Species Standard.Reconociendo que era imperativo evaluar la recuperación de especies y el impacto de la conservación, la Unión Internacional para la Conservación de la Naturaleza (UICN) convocó en 2012 al desarrollo de una “Lista Verde de Especies” (ahora el Estatus Verde de las Especies de la UICN). Un marco de referencia preliminar de una Lista Verde de Especies para evaluar el progreso de las especies hacia la recuperación, publicado en 2018, proponía 2 componentes separados pero interconectados: un método estandarizado (i.e., medición en relación con puntos de referencia de la viabilidad de especies, funcionalidad y distribución antes del impacto) para determinar el estatus de recuperación actual (puntuación de recuperación de la especie) y la aplicación de ese método para estimar impactos en el pasado y potenciales de conservación basados en 4 medidas (legado de conservación, dependencia de conservación, ganancia de conservación y potencial de recuperación). Probamos el marco de referencia con 181 especies representantes de diversos taxa, historias de vida, biomas, y categorías (riesgo de extinción) en la Lista Roja de la IUCN. Con base en la distribución observada de la puntuación de recuperación de las especies, proponemos las siguientes categorías de recuperación de la especie: totalmente recuperada, ligeramente mermada, moderadamente mermada, mayormente mermada, gravemente mermada, extinta en estado silvestre, e inderterminada. Cincuenta y nueve por ciento de las especies se consideraron mayormente o gravemente mermada. Aunque hubo una relación negativa entre el riesgo de extinción y la puntuación de recuperación de la especie, la variación fue considerable. Algunas especies en las categorías de riesgo bajas fueron evaluadas como más lejos de recuperarse que aquellas con alto riesgo. Esto enfatiza que la recuperación de especies es diferente conceptualmente al riesgo de extinción y refuerza la utilidad del Estado Verde de las Especies de la UICN para comprender integralmente el estatus de conservación de especies. Aunque el riesgo de extinción no predijo el legado de conservación, la dependencia de conservación o la ganancia de conservación, se correlacionó positivamente con la potencial de recuperación. Solo 1.7% de las especies probadas fue categorizado como cero en los 4 indicadores de impacto de la conservación, lo que indica que la conservación ha jugado, o jugará, un papel en la mejoría o mantenimiento del estatus de la especie la gran mayoría de ellas. Con base en nuestros resultados, diseñamos una versión actualizada del marco de referencia para la evaluación que introduce la opción de utilizar una línea de base dinámica para evaluar los impactos futuros de la conservación en el corto plazo y redefine corto plazo como 10 años.